Providing an Optimal Model in Modeling the Dependence Structure of the Elements of Financial Systems Using an Approach Based on Vine-Copula Functions. (Case Study: Market and Industry Indices at Tehran Stock Exchange)

Identification of the structure of dependence among different elements of a financial system has long been a hot topic to researchers due to its impact on the financial asset risk assessment. Currently, the capital market is one of the key financial systems in Iran’s economy, making the understanding and identification of its intra-system associations a major concern to investors and investment managers who seek to forecast future conditions. Accordingly, the present research investigates and models the dependence structure of different market indices of the Tehran Stock Exchange (TSE), as a representative of the country’s financial system, and the indices referring to the active industries in the TSE, as a component of the financial system. We herein investigated a total of 10 market indices and 31 other indices referring to the most significant active industries in the TSE. The mentioned industries were clustered based on three distinctive scenarios. Considering the number of components and the abnormal structure of their distributions and also taking into account the importance of marginal distributions in the assessment of the system component dependence structure model, we found the copula functions as a useful tool for expressing the dependence between different variables. In this research, the dependence structure of the market and industry indices of the TSE was investigated using two subroutines of the vine-copula functions, namely C-Vine and R-Vine. The results were then studied using Vuong’s test. The outcomes indicated that the C-Vine functions can generate very good fits to the dependence structures among various industry indices. Moreover, the best fits could be explained using the t-student family of the copula functions. Based on the results of the present study, it is possible to evaluate the relationships between industry indicators and the impact of different market industries from changes in the whole market and make optimal decisions in choosing the portfolio composition.